The CSV file and charts on this site show a relative volume of directions requests per country/region or city compared to a baseline volume on January 13th, 2020.

We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, the relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data.

Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population.

To get an overview we first load the data into R and print the available regions (data for countries and many cities are available) and transportation types (“driving”, “transit” and “walking”):

mobility

We now create a function mobi_trends to return the data in a well-structured format. The default plot = TRUE plots the data, plot = FALSE returns a named vector with the raw data for further investigation:

mobi_trends

The drop is quite dramatic… by 60%! Even more dramatic, of course, is the situation in Italy:

mobi_trends(reg = "Italy")

A drop by 80%! The same plot for Frankfurt:

mobi_trends(reg = "Frankfurt")

Obviously in Germany people are taking those measures less seriously lately, there seems to be a clear upward trend. This can also be seen in the German “walking” data:

mobi_trends(reg = "Germany", trans = "walking")

What is interesting is that before the lockdown “transit” mobility seems to have accelerated before plunging:

mobi_trends(reg = "Germany", trans = "transit")

You can also plot the raw numbers only, without an added smoother (option addsmooth = FALSE):

mobi_trends(reg = "London", trans = "walking", addsmooth = FALSE)

And as I said, you can conduct your own analyses on the formatted vector of the time series (option plot = FALSE)…

…as we have only scratched the surface of the many possibilities here, there are many interesting analyses, like including the data in epidemiological models or simply calculate correlations with new infections/deaths: please share your findings in the comments below!